The state of data quality 2022 survey by Monte Carlo – In brief

• Outlines data issues today by leading data practitioners
• Highlights the huge data bottlenecks and possible solutions

This survey is ideal for the following:

The survey is written with all data practitioners in mind but is especially good for the following:

• Data engineers
• Data scientists
• CDO/CIO/CGO/CEO

They can understand the data issues and bottlenecks that need to be addressed.

The survey

Data creation and ingestion are growing at a considerable rate. Most enterprises realise the value and wealth of insights buried in their data. Once consumed and processed with well-designed AI/analytics, the data could influence every decision, interaction and process in the future. However, the key is not necessarily the volume but the quality of that data.

It is often said that most data engineers/data scientists spend much time wrangling with the data. This can be costly but has this changed?  This influential State of Data 2022 survey from Bar Moses and the Monte Carlo team, assisted with other data luminaries like Joe Reis (Ternary data), shines an expert light on the state of data today and possible solutions.

Key points

• 40% of the working day is spent directly on data quality.
• Most errors/issues take 4+ hrs to detect and 9+ hours to resolve
• On average, will work on 61 incidents a month on data issues
• 58% of respondents said that the number of incidents has grown since last year
• Most say that bad data impacts 26% of their revenue
• Biggest obstacles to data quality – Budget, Data stack complexity, Executive buy-in
• Lots more…

Key takeaway and insights
This is a fascinating survey with shocking statistics. It shows how critical analysis time is spent on processing/fixing data by data engineers and data scientists. This time is too high and needs to be addressed to maximise value and opportunity. Teams still seem to need help with quality issues and fixing data problems. Data issues are being spotted late, but they also take a massive chunk of time to heal. There is no question that this will affect the company’s bottom line and should be addressed.

What is needed:

• Drive towards high-quality data, faster with little human intervention as possible
• Have a unified data AI strategy that reflects clear business objectives
• Concentrate on the quality of data from the source and eliminating bottlenecks in processing
• Making good data governance and observability a key priority
• Keep monitoring and improving as quickly and efficiently as possible
• Getting complete buy-in and investment to help get the maximum value out of the data possible

An excellent survey by the Mont Carlo team has raised some critical issues about data.

Read the survey here (I may need your details)

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